@inproceedings{hofmann-etal-2020-individual,
    title = "Individual corpora predict fast memory retrieval during reading",
    author = {Hofmann, Markus J.  and
      M{\"u}ller, Lara  and
      R{\"o}lke, Andre  and
      Radach, Ralph  and
      Biemann, Chris},
    booktitle = "Proceedings of the Workshop on the Cognitive Aspects of the Lexicon",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.cogalex-1.1",
    pages = "1--11",
    abstract = "The corpus, from which a predictive language model is trained, can be considered the experience of a semantic system. We recorded everyday reading of two participants for two months on a tablet, generating individual corpus samples of 300/500K tokens. Then we trained word2vec models from individual corpora and a 70 million-sentence newspaper corpus to obtain individual and norm-based long-term memory structure. To test whether individual corpora can make better predictions for a cognitive task of long-term memory retrieval, we generated stimulus materials consisting of 134 sentences with uncorrelated individual and norm-based word probabilities. For the subsequent eye tracking study 1-2 months later, our regression analyses revealed that individual, but not norm-corpus-based word probabilities can account for first-fixation duration and first-pass gaze duration. Word length additionally affected gaze duration and total viewing duration. The results suggest that corpora representative for an individual{'}s long-term memory structure can better explain reading performance than a norm corpus, and that recently acquired information is lexically accessed rapidly.",
}